The standard approach of most MOT algorithms is referred to as “tracking-by-detection", \(i.e.\)  the tracking is done by iteratively detecting objects successively across the frames of a video.
The bounding boxes of two successive frames are usually associated together to assign the same target ID to the bounding boxes that contain the same object.
Recently, many methods that have been developed can detect objects with very good accuracy and precision, \(e.g.\)  Faster-Region based Convolutional Neural Networks (RCNN) [1]}, Single Shot Detector [2]}, You Only Look Once (YOLO) [3]}, YOLO v2 [4]}, YOLO v3 [5]}, YOLO v4 [6]}, Mask Region-based Convolutional Neural Network (R-CNN) [7]}, and Region-based Fully Connected Networks (R-FCN) [8]}.
Since these methods can already provide very good detections, most of these algorithms focus on developing better associating algorithms to track objects with the same target ID.
These algorithms are developed for large objects, \(e.g.\)  a pedestrian, while we aim to track small objects with low resolutions, \(e.i.\)  skin features.
This makes our problem inherently more difficult as we have less local information and less distinctive features (patterns/ structures) to identify our target in the images; leading to a possible increase in false positive cases as there could be many parts of the image with a similar appearance.
